REAL-TIME LICENSING MANAGEMENT OF SECURE MULTIMEDIA CONTENT DELIVE RY

Media Commerce is now becoming a new trend which results fr om faster development of network bandwidth and high availability of multimedia t echnologies, how to protect media content from being used in a right-violated w ay is one of most important issues to take into account. In this paper, a novel and efficient authorization and authentication Digital Rights Management (DRM) s chema is proposed firstly for secure multimedia delivery, then based on the sche ma, a real-time digital signature algorithm built on Elliptic Curve Cryptograph y (ECC) is adopted for fast authentication and verification of licensing managem ent, thus secure multimedia delivery via TCP/RTP can efficiently work with real -time transaction response and high Quality of Service (QoS) . Performance eval uations manifest the proposed schema is secure, available for real-time media s tream authentication and authorization without much effected of QoS. The propose d schema is not only available for Client/Server media service but can be easily extended to P2P and broadcasting network for trusted rights management.

A Time Pattern-Based Intelligent Cache Optimization Policy on Korea Advanced Research Network

Data is growing quickly due to a significant increase in social media applications.Today,billions of people use an enormous amount of data to access the Internet.The backbone network experiences a substantial load as a result of an increase in users.Users in the same region or company frequently ask for similar material,especially on social media platforms.The subsequent request for the same content can be satisfied from the edge if stored in proximity to the user.Applications that require relatively low latency can use Content Delivery Network(CDN)technology to meet their requirements.An edge and the data center con-stitute the CDN architecture.To fulfill requests from the edge and minimize the impact on the network,the requested content can be buffered closer to the user device.Which content should be kept on the edge is the primary concern.The cache policy has been optimized using various conventional and unconventional methods,but they have yet to include the timestamp beside a video request.The 24-h content request pattern was obtained from publicly available datasets.The popularity of a video is influenced by the time of day,as shown by a time-based video profile.We present a cache optimization method based on a time-based pat-tern of requests.The problem is described as a cache hit ratio maximization pro-blem emphasizing a relevance score and machine learning model accuracy.A model predicts the video to be cached in the next time stamp,and the relevance score identifies the video to be removed from the cache.Afterwards,we gather the logs and generate the content requests using an extracted video request pattern.These logs are pre-processed to create a dataset divided into three-time slots per day.A Long short-term memory(LSTM)model is trained on this dataset to forecast the video at the next time interval.The proposed optimized caching policy is evaluated on our CDN architecture deployed on the Korean Advanced Research Network(KOREN)infrastructure.Our findings demonstrate how add-ing time-based request patterns impacts the system by increasing the cache hit rate.To show the effectiveness of the proposed model,we compare the results with state-of-the-art techniques.